22 Feb 2021

22 Feb 2021

Review status: a revised version of this preprint is currently under review for the journal NHESS.

Evaluation of Mei-yu Heavy-Rainfall Quantitative Precipitation Forecasts in Taiwan by A Cloud-Resolving Model for Three Seasons of 2012–2014

Chung-Chieh Wang1, Pi-Yu Chuang1, Chih-Sheng Chang1, Kazuhisa Tsuboki2, Shin-Yi Huang1, and Guo-Chen Leu3 Chung-Chieh Wang et al.
  • 1Department of Earth Sciences, National Taiwan Normal University, Taipei, Taiwan
  • 2Institute for Space-Earth Environmental Research, Nagoya University, Nagoya, Japan
  • 3Central Weather Bureau, Taipei, Taiwan

Abstract. In this study, the performance of quantitative precipitation forecasts (QPFs) by the Cloud-Resolving Storm Simulator (CReSS) in real-time in Taiwan, at a horizontal grid spacing of 2.5 km and a domain size of 1500 x 1200 km2, within a range of 72 h during three mei-yu seasons of 2012–2014 is evaluated using categorical statistics, with an emphasis on heavy events (≥ 100 mm per 24 h). The overall threat scores (TSs) of QPFs for all events on day 1 (0–24 h) are 0.18, 0.15, and 0.09 at the threshold of 100, 250, and 500 mm, respectively, and indicate considerable improvements compared to past results and 5-km models.

Moreover, the TSs are shown to be higher and the model more skillful in predicting larger events, in agreement with earlier findings for typhoons. After classification based on observed rainfall, the TSs of day-1 QPFs for the largest 4 % of events by CReSS at 100, 250, and 500 mm (per 24 h) are 0.34, 0.24, and 0.16, respectively, and can reach 0.15 at 250 mm on day 2 (24–48 h) and 130 mm on day 3 (48–72 h). The larger events also exhibit higher probability of detection and lower false alarm ratio than weaker events almost without exception across all thresholds.

The strength of the model lies mainly in the topographic rainfall in Taiwan rather than migratory events that are less predictable. Our results highlight the crucial importance of cloud-resolving capability and the size of fine mesh for heavy-rainfall QPFs in Taiwan.

Chung-Chieh Wang et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2020-397', Anonymous Referee #1, 15 Mar 2021
  • CC1: 'Comment on nhess-2020-397', G. T.-J. Chen, 19 Mar 2021
  • RC2: 'Comment on nhess-2020-397', Anonymous Referee #2, 16 Jul 2021

Chung-Chieh Wang et al.

Chung-Chieh Wang et al.


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Short summary
This study indicated that CReSS model significantly improved heavy rainfall quantitative precipitation forecasts in Taiwan Mei-yu season. At high resolution, the model has higher threat scores and more skillful in predicting larger rainfall events compared to smaller ones. And the strength of model mainly lies in the topographic rainfall rather than less predictable and migratory events due to nonlinearity.